Prefix indexing

ABSTRACT

A table organized into a set of batch units is accessed. A set of N-grams are generated for a data value in the source table. The set of N-grams include a first N-gram of a first length and a second N-gram of a second length where the first N-gram corresponds to a prefix of the second N-gram. A set of fingerprints are generated for the data value based on the set of N-grams. The set of fingerprints include a first fingerprint generated based on the first N-gram and a second fingerprint generated based on the second N-gram and the first fingerprint. A pruning index that indexes distinct values in each column of the source table is generated based on the set of fingerprints and stored in a database with an association with the source table.

PRIORITY CLAIM

This application is a Continuation of U.S. patent application Ser. No.17/484,817, entitled “PREFIX N-GRAM INDEXING,” filed Sep. 24, 2021,which is a Continuation-in-part of U.S. patent application Ser. No.17/388,160, entitled “PRUNING INDEX GENERATION FOR PATTERN MATCHINGQUERIES,” filed on Jul. 29, 2021, which is a Continuation of U.S. patentapplication Ser. No. 17/218,962, entitled “GENERATION OF PRUNING INDEXFOR PATTERN MATCHING QUERIES”, filed Mar. 31, 2021, which is aContinuation of U.S. patent application Ser. No. 17/086,228, entitled“PRUNING INDEX FOR OPTIMIZATION OF PATTERN MATCHING QUERIES”, filed Oct.30, 2020, now issued as U.S. Pat. No. 10,997,719, which claims priorityto U.S. Provisional Patent Application No. 63/084,394 filed on Sep. 28,2020 and is a continuation-in-part of U.S. patent application Ser. No.16/932,462, entitled “DATABASE QUERY PROCESSING USING A PRUNING INDEX,”filed on Jul. 17, 2020, now issued as U.S. Pat. No. 10,942,925, which isa continuation of U.S. patent Ser. No. 16/727,315, entitled “PRUNINGINDEXES TO ENHANCE DATABASE QUERY PROCESSING,” filed on Dec. 26, 2019,now issued as U.S. Pat. No. 10,769,150, the contents of which areincorporated herein by reference in their entireties. This applicationalso claims priority to U.S. Provisional Patent Application No.63/260,874 filed on Sep. 3, 2021, the contents of which are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to databases and, morespecifically, to using prefix indexing to optimize processing of queriesin a database system.

BACKGROUND

When certain information is to be extracted from a database, a querystatement may be executed against the database data. A database systemprocesses the query and returns certain data according to one or morequery predicates that indicate what information should be returned bythe query. The database system extracts specific data from the databaseand formats that data into a readable form. However, it can bechallenging to execute queries on a very large table because asignificant amount of time and computing resources are required to scanan entire table to identify data that satisfies the query.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based database system in communication with a cloud storageprovider system, in accordance with some embodiments of the presentdisclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is a conceptual diagram illustrating generation of an exampleblocked bloom filter, which may form part of a pruning index, inaccordance with some example embodiments.

FIG. 5 illustrates a portion of an example pruning index, in accordancewith some embodiments of the present disclosure.

FIG. 6 is a conceptual diagram illustrating further details regardingthe creation of an example pruning index, in accordance with someembodiments.

FIG. 7 is a conceptual diagram illustrating maintenance of a pruningindex, in accordance with some embodiments.

FIGS. 8-12 are flow diagrams illustrating operations of thenetwork-based database system in performing a method for generating andusing a pruning index in processing a database query, in accordance withsome embodiments of the present disclosure.

FIG. 13 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description to provide a thoroughunderstanding of the subject matter. It will be understood that theseexamples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

As noted above, processing queries directed to very large tables ischallenging because a significant amount of time and computing resourcesare required to scan an entire table to identify data that satisfies thequery. Therefore, it can be desirable to execute a query withoutscanning the entire table. Aspects of the present disclosure address theabove and other challenges in processing queries on large tables bycreating a pruning index that may be used to construct a reduced scanset for processing a query. More specifically, a large source table maybe organized into a set of batch units such as micro-partitions, and apruning index can be created for the source table to be used inidentifying a subset of the batch units to scan to identify data thatsatisfies the query.

As discussed herein, a “micro-partition” is a batch unit, and eachmicro-partition has contiguous units of storage. By way of example, eachmicro-partition may contain between 50 MB and 500 MB of uncompresseddata (note that the actual size in storage may be smaller because datamay be stored compressed). Groups of rows in tables may be mapped intoindividual micro-partitions organized in a columnar fashion. This sizeand structure allow for extremely granular selection of themicro-partitions to be scanned, which can be comprised of millions, oreven hundreds of millions, of micro-partitions. This granular selectionprocess for micro-partitions to be scanned is referred to herein as“pruning.” Pruning involves using metadata to determine which portionsof a table, including which micro-partitions or micro-partitiongroupings in the table, are not pertinent to a query, and then avoidingthose non-pertinent micro-partitions when responding to the query andscanning only the pertinent micro-partitions to respond to the query.Metadata may be automatically gathered about all rows stored in amicro-partition, including: the range of values for each of the columnsin the micro-partition; the number of distinct values; and/or additionalproperties used for both optimization and efficient query processing. Inone embodiment, micro-partitioning may be automatically performed on alltables. For example, tables may be transparently partitioned using theordering that occurs when the data is inserted/loaded. However, itshould be appreciated that this disclosure of the micro-partition isexemplary only and should be considered non-limiting. It should beappreciated that the micro-partition may include other database storagedevices without departing from the scope of the disclosure.

Consistent with some embodiments, a network-based database systemgenerates a pruning index for a source table and uses the pruning indexto prune micro-partitions of the source table when processing queriesdirected to the source table. In generating a pruning index, thenetwork-based database system generates a filter for eachmicro-partition of the source table that indexes distinct values (ordistinct N-grams) in each column of the micro-partition of the sourcetable. The filter may, for example, comprise a blocked bloom filter, abloom filter, a hash filter, or a cuckoo filter.

In general, the pruning index includes a probabilistic data structurethat stores fingerprints (e.g., bit patterns) for all searchable valuesin a source table. The fingerprints are based on hashes computed basedon searchable values in the source table. In some embodiments, thefingerprints are based on a hash computed based on N-grams ofpreprocessed variants of each searchable value in the source table.

Fingerprints are computed for all N-grams that are generated for eachsearchable value. For a given value, the database system generates a setof N-grams by breaking the value into multiple segments of N-length. Inan example, the value of N is three and the searchable value is“solution.” In this example, the database system computes fingerprintsfor “sol”, “olu”, “lut,” “uti”, “tio”, and “ion”. Depending on theembodiment, a single value for N may be used in generating the set ofN-grams, or multiple values of N can be used. That is, the N-grams inthe set of N-grams may be the same size or there may be multiple sizesof N-grams in the set.

Generating the pruning index using multiple values of N can be costly interms of index lookups and storage. To address the cost issue related touse of multiple values of N, the database system uses an approach thatutilizes a prefix property that exists between N-grams of differentsizes that start at the same offset in the indexed text. In the previousexample of the searchable value of “solution,” the database systemconstructs the fingerprints for “solut” (e.g., where N=5) and “soluti”(e.g., where N=6) in such a way that the fingerprint of “soluti”contains a superset of the bits in the fingerprint of its prefix“solut”. This approach, where the fingerprint of the larger N-grams isbased on the fingerprint of the smaller N-grams that are prefixessignificantly reduces indexing and storage costs. This approach alsoprovides the benefit of providing a form of prefix compression for thefilters in the pruning index.

In a more specific example of prefix N-gram indexing, the searchablevalue is “testvalue” and N-grams are generated using N=5, 6, 7, and 8.More specifically, for: N=5, a first N-gram “testy” is generated; N=6, asecond N-gram “testva” is generated; N=7, a third N-gram “testva” isgenerated; and N=8, a fourth N-gram “testvalu” is generated. With N-gramindexing, the database system uses a constructive approach that movesfrom smaller to larger values of N by using the prefix property. Withspecific reference to the example, the database system starts with thesmallest value of N (N=5) and computes an initial hash based on theN-gram generated for that value (e.g., hash5=compute_hash(“testy”, 0)).The database system uses the initial hash to produce an initialfingerprint that is used to populate a filter in the pruning index. Theinitial hash can also be used to determine a particular filter in thepruning index to be populated with all N-grams that share the “testy”prefix. In embodiments which rely on a blocked bloom filter scheme forgenerating the pruning index, the initial hash is also used to determinea block to be populated with all N-grams that share the “testy” prefix.

In generating the fingerprint for the second N-gram in the set where N=6(“testva”), the database system computes a hash over the newly addedcharacters. That is, the database system computes a hash over a portionof the second N-gram that excludes the first N-gram. In computing thehash, the database system uses the initial fingerprint generated for thefirst N-gram as a seed to the hashing function (e.g., hash6=compute_hash(“a”, hash5). Seeding with the hash from the previous step provides theprefix property, expressing that the new character(s) were preceded byall that was hashed in previous steps.

The second fingerprint is used to populate the same filter (or morespecifically, the bloom filter block) as the initial fingerprint. Asnoted above, the filter (or more specifically, the bloom filter block)is determined by the fingerprint generated from the smallest N-gramthereby maintaining low lookup costs for the pruning index given thatregardless of the value of N, the same filter (or block) can be scanned.

The database system continues the process set forth above, adding morebits to the same filter (or block) for all remaining values of N fromthe same offset that are to be indexed. The number of bits added at eachstep can be fixed or based on the value of N. For example, the number ofbits added at each step can be gradually decreased as the value of Nincreases (e.g., 6 bits for N=5, 4 bits for N=6, 2 bits for N=7, and 1bit for N=8). After generating a fingerprint for each of the abovereferenced N-grams, the database system then moves to the next offset inthe indexed value to start the process for the next set of N-grams untilthe input is exhausted.

It shall be appreciated that the prefix indexing approach used by thedatabase system is not limited to consecutive N-gram sizes. The prefixindexing approach can be applied for indexing hierarchical relationshipsin general, for example to index the hierarchy into a blocked bloomfilter or other hash-based data structures. As an example, the approachcan be extended such that an initial fingerprint is generated for anitem in the highest level in the hierarchy (for N-grams, this is thebase prefix). The hash of this root item determines (i) the filter (orspecific filter block) into which all fingerprints for items that arechildren to the root item go and (ii) the initial fingerprint to use topopulate the filter. Child items of this root on all the consecutivelevels in the hierarchy contribute additional fingerprint-bits(typically less bits the lower they are in the hierarchy) into theselected block. The hash that determines the bits for each item is basedon the hash of the direct root item as a seed for the hashing function.This establishes a connection between the hashes through their seeds andthe connection propagates from root to leaf and encodes a representationof the hierarchy into the filter.

As an example of the foregoing approach to indexing hierarchicalrelationships, assume that there are two streams of prefix n-gramindexing, one that captures N=5, 6, 7, 8, and another one that considersonly non-overlapping 8-grams. The second stream can use the final 8-gramhash from the first stream to capture the prefix relationship withneighboring, non-overlapping 8-grams. In essence, this would beequivalent to indexing N=5, 6, 7, 8, 16.

For a given query, the pruning index can be used to quickly disqualifymicro-partitions that are certain to not include data that satisfies thequery. When a query is received, rather than scanning the entire sourcetable to identify matching data, the network-based database systemprobes the pruning index to identify a reduced scan set ofmicro-partitions comprising only a subset of the micro-partitions of thesource table, and only the reduced scan set of micro-partitions isscanned when executing the query.

The database system can use a pruning index to prune a scan set forqueries with equality predicates (e.g., “=”) as well as queries withpattern matching predicates (e.g., LIKE, ILIKE, CONTAINS, STARTSWITH,ENDSWITH, etc.).

For a given equality predicate, the database system uses the pruningindex to identify a subset of micro-partitions to scan for data thatcompletely matches an entire string or other searchable value. For agiven pattern matching predicate, the database system uses the pruningindex to identify a set of micro-partitions to scan for data thatmatches a specified search pattern, which can include one or morepartial strings and one or more wildcards (e.g., “%” or “_”) used torepresent wildcard character positions in the pattern (e.g., characterpositions whose underlying value unconstrained by the query).

By using a pruning index to prune the set of micro-partitions to scan inexecuting a query, the database system accelerates the execution ofpoint queries on large tables when compared to conventionalmethodologies. Using a pruning index in this manner also guarantees aconstant overhead for every searchable value on the table. Additionalbenefits of pruning index utilization include, but are not limited to,an ability to support multiple predicate types, an ability to quicklycompute the number of distinct values in a table, and the ability tosupport join pruning.

FIG. 1 illustrates an example computing environment 100 that includes adatabase system 102 in communication with a storage platform 104, inaccordance with some embodiments of the present disclosure. To avoidobscuring the inventive subject matter with unnecessary detail, variousfunctional components that are not germane to conveying an understandingof the inventive subject matter have been omitted from FIG. 1. However,a skilled artisan will readily recognize that various additionalfunctional components may be included as part of the computingenvironment 100 to facilitate additional functionality that is notspecifically described herein.

As shown, the computing environment 100 comprises the database system102 and a storage platform 104 (e.g., AWS®, Microsoft Azure BlobStorage®, or Google Cloud Storage®). The database system 102 is used forreporting and analysis of integrated data from one or more disparatesources including storage devices 106-1 to 106-N within the storageplatform 104. The storage platform 104 comprises a plurality ofcomputing machines and provides on-demand computer system resources suchas data storage and computing power to the database system 102.

The database system 102 comprises a compute service manager 108, anexecution platform 110, and a database 114. The database system 102hosts and provides data reporting and analysis services to multipleclient accounts. Administrative users can create and manage identities(e.g., users, roles, and groups) and use permissions to allow or denyaccess to the identities to resources and services.

The compute service manager 108 coordinates and manages operations ofthe database system 102. The compute service manager 108 also performsquery optimization and compilation as well as managing clusters ofcomputing services that provide compute resources (also referred to as“virtual warehouses”). The compute service manager 108 can support anynumber of client accounts such as end users providing data storage andretrieval requests, system administrators managing the systems andmethods described herein, and other components/devices that interactwith compute service manager 108.

The compute service manager 108 is also in communication with a userdevice 112. The user device 112 corresponds to a user of one of themultiple client accounts supported by the database system 102. In someembodiments, the compute service manager 108 does not receive any directcommunications from the user device 112 and only receives communicationsconcerning jobs from a queue within the database system 102.

The compute service manager 108 is also coupled to database 114, whichis associated with the data stored in the computing environment 100. Thedatabase 114 stores data pertaining to various functions and aspectsassociated with the database system 102 and its users. In someembodiments, the database 114 includes a summary of data stored inremote data storage systems as well as data available from a localcache. Additionally, the database 114 may include information regardinghow data is organized in remote data storage systems (e.g., the storageplatform 104) and the local caches. The database 114 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

For example, the database 114 can include one or more pruning indexes.The compute service manager 108 may generate a pruning index for eachsource table accessed from the storage platform 104 and use a pruningindex to prune the set of micro-partitions of a source table to scan fordata in executing a query. That is, given a query directed at a sourcetable organized into a set of micro-partitions, the computing servicemanger 108 can access a pruning index from the database 114 and use thepruning index to identify a reduced set of micro-partitions to scan inexecuting the query. The set of micro-partitions to scan in executing aquery may be referred to herein as a “scan set.”

In some embodiments, the compute service manager 108 may determine thata job should be performed based on data from the database 114. In suchembodiments, the compute service manager 108 may scan the data anddetermine that a job should be performed to improve data organization ordatabase performance. For example, the compute service manager 108 maydetermine that a new version of a source table has been generated andthe pruning index has not been refreshed to reflect the new version ofthe source table. The database 114 may include a transactional changetracking stream indicating when the new version of the source table wasgenerated and when the pruning index was last refreshed. Based on thattransaction stream, the compute service manager 108 may determine that ajob should be performed. In some embodiments, the compute servicemanager 108 determines that a job should be performed based on a triggerevent and stores the job in a queue until the compute service manager108 is ready to schedule and manage the execution of the job. In anembodiment of the disclosure, the compute service manager 108 determineswhether a table or pruning index needs to be reclustered based on one ormore DML commands being performed, wherein one or more of DML commandsconstitute the trigger event.

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to storage platform 104 of the storage platform 104. Thestorage platform 104 comprises multiple data storage devices 106-1 to106-N. In some embodiments, the data storage devices 106-1 to 106-N arecloud-based storage devices located in one or more geographic locations.For example, the data storage devices 106-1 to 106-N may be part of apublic cloud infrastructure or a private cloud infrastructure. The datastorage devices 106-1 to 106-N may be hard disk drives (HDDs), solidstate drives (SSDs), storage clusters, Amazon S3™ storage systems or anyother data storage technology. Additionally, the storage platform 104may include distributed file systems (e.g., Hadoop Distributed FileSystems (HDFS)), object storage systems, and the like.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete micro-partition files using a least recently used (LRU) policyand implement an out of memory (00M) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, the data storage devices 106-1 to 106-N aredecoupled from the computing resources associated with the executionplatform 110. This architecture supports dynamic changes to the databasesystem 102 based on the changing data storage/retrieval needs as well asthe changing needs of the users and systems. The support of dynamicchanges allows the database system 102 to scale quickly in response tochanging demands on the systems and components within the databasesystem 102. The decoupling of the computing resources from the datastorage devices supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources.

The compute service manager 108, database 114, execution platform 110,and storage platform 104 are shown in FIG. 1 as individual discretecomponents. However, each of the compute service manager 108, database114, execution platform 110, and storage platform 104 may be implementedas a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations). Additionally, eachof the compute service manager 108, database 114, execution platform110, and storage platform 104 can be scaled up or down (independently ofone another) depending on changes to the requests received and thechanging needs of the database system 102. Thus, in the describedembodiments, the database system 102 is dynamic and supports regularchanges to meet the current data processing needs.

During typical operation, the database system 102 processes multiplejobs determined by the compute service manager 108. These jobs arescheduled and managed by the compute service manager 108 to determinewhen and how to execute the job. For example, the compute servicemanager 108 may divide the job into multiple discrete tasks and maydetermine what data is needed to execute each of the multiple discretetasks. The compute service manager 108 may assign each of the multiplediscrete tasks to one or more nodes of the execution platform 110 toprocess the task. The compute service manager 108 may determine whatdata is needed to process a task and further determine which nodeswithin the execution platform 110 are best suited to process the task.Some nodes may have already cached the data needed to process the taskand, therefore, be a good candidate for processing the task. Metadatastored in the database 114 assists the compute service manager 108 indetermining which nodes in the execution platform 110 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 110 process the task using datacached by the nodes and, if necessary, data retrieved from the storageplatform 104. It is desirable to retrieve as much data as possible fromcaches within the execution platform 110 because the retrieval speed istypically much faster than retrieving data from the storage platform104.

As shown in FIG. 1, the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices106-1 to 106-N in the storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 106-1 to 106-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, the compute service manager 108 includesan access manager 202 and a key manager 204 coupled to a data storagedevice 206. Access manager 202 handles authentication and authorizationtasks for the systems described herein. Key manager 204 manages storageand authentication of keys used during authentication and authorizationtasks. For example, access manager 202 and key manager 204 manage thekeys used to access data stored in remote storage devices (e.g., datastorage devices in storage platform 104). As used herein, the remotestorage devices may also be referred to as “persistent storage devices”or “shared storage devices.”

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata necessary to process a received query (e.g., a data storage requestor data retrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214 and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 214 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor216 executes the execution code for jobs received from a queue ordetermined by the compute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 218 determines a priority for internaljobs that are scheduled by the compute service manager 108 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 110. In some embodiments, the job scheduler andcoordinator 218 identifies or assigns particular nodes in the executionplatform 110 to process particular tasks. A virtual warehouse manager220 manages the operation of multiple virtual warehouses implemented inthe execution platform 110. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local caches(e.g., the caches in execution platform 110). The configuration andmetadata manager 222 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 224 overseeprocesses performed by the compute service manager 108 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 110. The monitor and workloadanalyzer 224 also redistribute tasks, as needed, based on changingworkloads throughout the database system 102 and may furtherredistribute tasks based on a user (e.g., “external”) query workloadthat may also be processed by the execution platform 110. Theconfiguration and metadata manager 222 and the monitor and workloadanalyzer 224 are coupled to a data storage device 226. Data storagedevice 226 in FIG. 2 represents any data storage device within thedatabase system 102. For example, data storage device 226 may representcaches in execution platform 110, storage devices in storage platform104, or any other storage device.

As shown, the compute service manager 108 further includes a pruningindex generator 228. The pruning index generator 228 is responsible forgenerating pruning indexes to be used in pruning scan sets for queriesdirected to tables stored in the storage platform 104. Each pruningindex comprises a set of filters (e.g., blocked bloom filters, bloomfilters, hash filter, or cuckoo filters) that encode an existence ofunique N-grams in each column of a source table. The pruning indexgenerator 228 generates a filter for each micro-partition of a sourcetable and each filter indicates whether data matching a query ispotentially stored on a particular micro-partition of the source table.Further details regarding the generation of pruning indexes arediscussed below.

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse N. Each virtual warehouse includesmultiple execution nodes that each includes a data cache and aprocessor. The virtual warehouses can execute multiple tasks in parallelby using the multiple execution nodes. As discussed herein, theexecution platform 110 can add new virtual warehouses and drop existingvirtual warehouses in real-time based on the current processing needs ofthe systems and users. This flexibility allows the execution platform110 to quickly deploy large amounts of computing resources when neededwithout being forced to continue paying for those computing resourceswhen they are no longer needed. All virtual warehouses can access datafrom any data storage device (e.g., any storage device in storageplatform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 106-1 to 106-N shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 106-1 to106-N and, instead, can access data from any of the data storage devices106-1 to 106-N within the storage platform 104. Similarly, each of theexecution nodes shown in FIG. 3 can access data from any of the datastorage devices 106-1 to 106-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternate embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in storageplatform 104. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the storage platform 104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and N are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and N areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 4 is a conceptual diagram illustrating generation of a filter 400,which forms part of a pruning index generated by the database system 102based on a source table 402, in accordance with some exampleembodiments. As shown, the source table 402 is organized into multiplemicro-partitions and each micro-partition comprises multiple columns inwhich values are stored.

In generating a pruning index, the compute service manager 108 generatesa filter for each micro-partition of the source table 402, an example ofwhich is illustrated in FIG. 4 as blocked bloom filter 400. Blockedbloom filter 400 comprises multiple bloom filters and encodes theexistence of distinct N-grams present in each column of thecorresponding micro-partition. When a query is received, rather thanscanning the entire source table 402 to evaluate query, the databasesystem 102 probes the pruning index to identify a reduced scan set ofmicro-partitions comprising only a subset of the micro-partitions of thesource table 402.

As shown, the blocked bloom filter 400 is decomposed into N bloomfilters stored as individual columns of the pruning index to leveragecolumnar scans. In generating the blocked bloom filter 400 for aparticular micro-partition of the source table 402, N-grams of storedvalues or preprocessed variants thereof are transformed into bitpositions in the bloom filters. For example, a set of fingerprints(e.g., hash values) can be generated from N-grams of stored values ineach column of the micro-partition and the set of fingerprints may beused to set bits in the bloom filters. Each line of the blocked bloomfilter 400 is encoded and stored as a single row in the pruning index.Each bloom filter 400 is represented in the pruning index as atwo-dimensional array indexed by the fingerprints of the N-grams of thestored column values.

FIG. 5 illustrates a portion of an example pruning index 500, inaccordance with some embodiments of the present disclosure. The examplepruning index 500 is organized into a plurality of rows and columns. Thecolumns of the pruning index 500 comprise a partition number 502 tostore a partition identifier and a blocked bloom filter 504 (e.g., theblocked bloom filter 400) that is decomposed into multiple numericcolumns, each column in the blocked bloom filter 504 represents a bloomfilter. To avoid obscuring the inventive subject matter with unnecessarydetail, various additional columns that are not germane to conveying anunderstanding of the inventive subject matter may have been omitted fromthe example pruning index 500 in FIG. 5.

FIG. 6 is a conceptual diagram illustrating creation of an examplepruning index, in accordance with some embodiments. The creation of afilter (e.g., a blocked bloom filter) is performed by a specializedoperator within the compute service manager 108 that computes the set ofrows of the pruning index. This operator obtains all the columns of aparticular micro-partition of a source table and populates the filterfor that micro-partition.

If the total number of distinct N-grams in the source table is unknown,the compute service manager 108 allocates a maximum number of levels tothe pruning index, populates each filter and then applies aconsolidation phase to merge the different filters in a finalrepresentation of the pruning index. The memory allocated to computethis information per micro-partition is constant. In the exampleillustrated in FIG. 6, the memory allocated to compute this informationis a two-dimensional array of unsigned integers. The first dimension isindexed by the level (maximum number of levels) and the second dimensionis indexed by the number of bloom filters. Since each partition isprocessed by a single thread, the total memory is bounded by the numberof threads (e.g., 8) and the maximum level of levels.

As shown in FIG. 6, at each partition boundary, the compute servicemanager 108 combines blocks based on a target bloom filter density. Forexample, the compute service manager 108 may combine blocks such thatthe bloom filter density is no more than half. Since the domain offingerprints (e.g., hashed values) is uniform, this can be doneincrementally or globally based on the observed number of distinctvalues computed above.

If the number of distinct values is known, the compute service manager108 determines the number of levels for the pruning index by dividingthe maximum number of distinct N-grams by the number of distinct N-gramsper level. To combine two levels, the compute service manager 108performs a logical OR on all the integers representing the filter.

For performance reasons, the filter functions (create and check) cancombine two hash functions (e.g., two 32-bit hash functions). Both thehash function computation and the filter derivation need to be identicalon both the execution platform 110 and compute service manager 108 toallow for pruning in compute service manager 108 and in the scan setinitialization in the execution platform 110.

FIG. 7 is a conceptual diagram illustrating maintenance of a pruningindex based on changes to a source table, in accordance with someembodiments. As shown, at 700, a change is made to a source table (e.g.,addition of one or more rows or columns). The change to the source tabletriggers generation of additional rows in the pruning index for eachchanged or new micro-partition of the source table, at 702. At a regularinterval, the newly produced rows in the pruning index are reclustered,at 704.

The compute service manager 108 uses a deterministic selection algorithmas part of clustering the prune index. The processing of eachmicro-partition in the source table creates a bounded (and mostlyconstant) number of rows based on the number of distinct N-grams in thesource micro-partition. By construction, those rows are known to beunique, and the index domain is non-overlapping for that partition andfully overlapping with already clustered index rows. To minimize thecost of clustering, the compute service manager 108 delays reclusteringof rows until a threshold number of rows has been produced to createconstant partitions.

Although the pruning index is described in some embodiments as beingimplemented specifically with blocked bloom filters, it shall beappreciated that the pruning index is not limited to blocked bloomfilters, and in other embodiments, the pruning index may be implementedusing other filters such as bloom filters, hash filters, or cuckoofilters.

FIGS. 8-12 are flow diagrams illustrating operations of the databasesystem 102 in performing a method 800 for generating and using a pruningindex in processing a database query, in accordance with someembodiments of the present disclosure. The method 800 may be embodied incomputer-readable instructions for execution by one or more hardwarecomponents (e.g., one or more processors) such that the operations ofthe method 800 may be performed by components of database system 102.Accordingly, the method 800 is described below, by way of example withreference thereto. However, it shall be appreciated that the method 800may be deployed on various other hardware configurations and is notintended to be limited to deployment within the database system 102.

Depending on the embodiment, an operation of the method 800 may berepeated in different ways or involve intervening operations not shown.Though the operations of the method 800 may be depicted and described ina certain order, the order in which the operations are performed mayvary among embodiments, including performing certain operations inparallel or performing sets of operations in separate processes. Forexample, although the use and generation of the pruning index aredescribed and illustrated together as part of the method 800, it shallbe appreciated that the use and generation of the pruning index may beperformed as separate processes, consistent with some embodiments.

At operation 805, the compute service manager 108 accesses a sourcetable that is organized into a plurality of micro-partitions. The sourcetable comprises a plurality of cells organized into rows and columns anda data value is included in each cell.

At operation 810, the compute service manager 108 generates a pruningindex based on the source table. The pruning index comprises a set offilters (e.g., a set of blocked bloom filters) that index distinctN-grams in each column of each micro-partition of the source table. Afilter is generated for each micro-partition in the source table andeach filter is decomposed into multiple numeric columns (e.g., 32numeric columns) to enable integer comparisons. Consistent with someembodiments, the pruning index comprises a plurality of rows and eachrow comprises at least a micro-partition identifier and a set of bloomfilters. Consistent with some embodiments, the compute service manager108 generates the pruning index in an offline process before receiving aquery. The compute service manager 108 stores the pruning index in adatabase with an association with the source table such that the pruningindex can be retrieved upon receiving a query directed at the sourcetable.

At operation 815, the compute service manager 108 receives a querydirected at the source table. The query can comprise an equalitypredicate (e.g., “=”) or a pattern matching predicate (e.g., LIKE,ILIKE, CONTAINS, STARTSWITH, or ENDSWITH). In instances in which thequery includes a pattern matching predicate, the query specifies asearch pattern for which matching stored data in the source table is tobe identified.

At operation 820, the compute service manager 108 accesses the pruningindex associated with the source table based on the query being directedat the source table. For example, the database 114 may store informationdescribing associations between tables and pruning indexes.

At operation 825, the compute service manager 108 uses the pruning indexto prune the set of micro-partitions of the source table to be scannedfor data that satisfies the query (e.g., a data value that satisfies theequality predicate or data that matches the search pattern). That is,the compute service manager 108 uses the pruning index to identify areduced scan set comprising only a subset of the micro-partitions of thesource table. The reduced scan set includes one or more micro-partitionsin which data that satisfies the query is potentially stored. The subsetof micro-partitions of the source table includes micro-partitionsdetermined to potentially include data that satisfies the query based onthe set of bloom filters in the pruning index.

At operation 830, the execution platform 110 processes the query. Inprocessing the query, the execution platform 110 scans the subset ofmicro-partitions of the reduced scan set while foregoing a scan of theremaining micro-partitions. In this way, the execution platform 110searches only micro-partitions where matching data is potentially storedwhile foregoing an expenditure of additional time and resources to alsosearch the remaining micro-partitions for which it is known, based onthe pruning index, that matching data is not stored.

Consistent with some embodiments, rather than providing a reduced scanset with micro-partitions of the source table to scan for data, thecompute service manager 108 may instead identify and compile a set ofnon-matching micro-partitions. The compute service manager 108 or theexecution platform 110 may remove micro-partitions from the scan setbased on the set of non-matching micro-partitions.

As shown in FIG. 9, the method 800 may, in some embodiments, furtherinclude operations 905 and 910. Consistent with these embodiments, theoperations 905 and 910 may be performed as part of the operation 810where the compute service manager 108 generates the pruning index. Theoperations 905 and 910 are described below in reference to a singlemicro-partition of the source table simply for ease of explanation.However, it shall be appreciated, that in generating the pruning index,the compute service manager 108 generates a filter for eachmicro-partitions of the source table and thus, the operations 905 and910 may be performed for each micro-partition of the source table.

At operation 905, the compute service manager 108 generates a filter fora micro-partition of the source table. For example, the compute servicemanager 108 may generate a blocked bloom filter for the micro-partitionthat indexes distinct N-grams in each column of the micro-partition ofthe source table. The filters are generated using a set of fingerprintsgenerated for each searchable data value in the micro-partition.

Consistent with some embodiments, for a given data value in themicro-partition, the compute service manager 108 can generate the set offingerprints based on a set of N-grams generated for the data value. Theset of N-grams can be generated based on the data value and/or one ormore preprocessed variants of the data value. The compute servicemanager 108 can generate a fingerprint based on a hash that is computedover an N-gram. In computing the hash, the compute service manager 108may utilize a rolling hash function or other known hashing scheme thatallows individual characters to be added or removed from a window ofcharacters. Each generated fingerprint is used to populate a cell in thefilter.

At operation 910, which is optional in some embodiments, the computeservice manager 108 merges one or more rows of the filter. The computeservice manager 108 can merge rows by performing a logical OR operation.The compute service manager 108 may merge rows of the filter until adensity threshold is reached, where the density refers to the ratio of1's and 0's in a row. The density threshold may be based on a targetfalse positive rate.

As shown in FIG. 10, the method 800 may, in some embodiments, includeoperations 1005, 1010, 1015, 1020, 1025, 1030, and 1035. Consistent withthese embodiments, the operations 1005, 1010, and 1015 may be performedprior to or as part of operation 810 where the compute service manager108 generates the pruning index for the source table. At operation 1005,the compute service manager 108 preprocesses the data values in thecells of the source table. In preprocesses a given data value, thecompute service manager 108 generates one or more preprocessed variantsof the data value. In performing the preprocessing, the compute servicemanager performs one or more normalization operations to a given datavalue. The compute service manager 108 can utilize one of several knownnormalization techniques to normalize data values (e.g., normalizationform canonical composition).

For a given data value, the preprocessing performed by the computeservice manager 108 can include, for example, any one or more of:generating a case-agnostic variant, (e.g., by converting uppercasecharacters to lowercase characters), generating one or more misspelledvariants based on common or acceptable misspellings of the data value,and generating one or more synonymous variants corresponding to synonymsof the data value. In general, in generating a preprocessed variant(e.g., case-agnostic variant, misspelled variant, a synonymous variantor a variant with special characters to indicate a start and end to adata value), the compute service manager 108 uses a common knowledgebase to transform a data value into one or more permutations of theoriginal data value.

As an example of the forgoing, the string “Bob” can be transformed intothe case-agnostic variant “bob.” As another example, the preprocessedvariants of “bob” “bbo” and “obb” can be generated for the string “Bob”to account for misspellings.

At operation 1010, the compute service manager 108 generates a set ofN-grams for each preprocessed variant. An N-gram in this context refersto a contiguous sequence of N-items (e.g., characters or words) in agiven value. For a given preprocessed variant of a data value in thesource table, the compute service manager 108 transforms the value intomultiple segments of N-length. For example, for a string, the computeservice manager 108 can transform the string into multiple sub-stringsof N-characters.

Depending on the embodiment, the value of N can be predetermined ordynamically computed at the time of generating the pruning index. Inembodiments in which the value of N is precomputed, the compute servicemanager 108 can determine an optimal value for N based on a data type ofvalues in the source table.

In some embodiments, the set of N-grams can include N-grams of differentsizes for a given data value. That is, multiple values of N can be usedin generating the set of N-grams. For example, for a given data value,the set of N-grams can include a first N-gram that is a first size(e.g., an N-gram generated using a first value of N) and a second N-gramthat is a second size (e.g., an N-gram generated using a second value ofN).

At operation 1015, the compute service manager 108 generates a set offingerprints for each set of N-grams. The compute service manager 108can generate a fingerprint by computing a hash over an N-gram or aportion thereof. In computing the hash, the compute service manager 108may utilize a rolling hash function or other known hashing scheme thatallows individual characters to be added or removed from a window ofcharacters. An example hash function used by the compute service manager108 is the XxHash( ) function, although other known hash functions canbe utilized. Each generated fingerprint can be used to populate a cellin the filter.

Consistent with these embodiments, the operations 1020, 1025, and 1030can be performed prior to or as part of operation 820 where the computeservice manager 108 prunes the scan set using the pruning index. Atoperation 1020, the compute service manager 108 preprocesses a searchpattern included in the query. In preprocessing the search pattern, thecompute service manager 108 performs the same preprocessing operationsthat are performed on the data values in the source table at 1005 toensure that the characters of the search pattern fit the pruning index.Hence, in preprocessing the search pattern, the compute service manager108 can perform any one or more of: generating a case-agnostic variantof the search pattern (e.g., by converting uppercase characters tolowercase characters), generating one or more misspelled variants basedon common or acceptable misspellings of the search pattern, generatingone or more synonymous variants corresponding to synonyms of the searchpattern, and generating a variant that include special characters tomark a start and end of the search pattern. In preprocessing a givenpattern, the compute service manager 108 can generate one or morepreprocessed variants of the search pattern. For example, the computeservice manager 108 can generate any one or more of: a case-agnosticvariant, misspelled variant, or a synonymous variant for the searchpattern. As a further example, the compute service manager 108 cangenerate a variant that includes special characters to indicate a startand end of a search pattern (e.g., “{circumflex over ( )}testvalue$” forthe search pattern “testvalue”).

At operation 1025, the compute service manager 108 generates a set ofN-grams for the search pattern based on the one or more preprocessedvariants of the search pattern. The compute service manager 108 uses thesame value for N that was used to generate the pruning index. Inembodiments in which the compute service manager 108 uses multiplevalues for N in generating the pruning index, the compute servicemanager 108 uses the same values for generating the set of N-grams forthe search pattern.

In an example, the query includes the following statement:

WHERE a ILIKE ‘%LoremIpsum%Dolor%Sit%Amet’

In this example, ‘%LoremIpsum%Dolor%Sit%Amet’ is the search pattern andin preprocessing the search pattern, the compute service manager 108converts the search pattern to all lower case to create a case-agnosticvariant: ‘%loremipsum%dolor%sit%amet’. The compute service manager 108splits the search pattern into segments at the wild card positions,which, in this example, produces the following sub-strings:“loremipsum”, “dolor”, “sit”, and “amet”. Based on these sub-strings,the compute service manager 108 generates the following set of N-grams:

-   -   Set [“lorem”, “oremi”, “remip”, “emips”, “mipsu”, “ipsum”,        “dolor”]        In this example N is 5, and thus the compute service manager 108        discards the sub-strings “sit” and “amet” as their length is        less than 5.

At operation 1030, the compute service manager 108 generates a set offingerprints based on each set of N-grams generated based on the searchpattern. As with the fingerprints generated based on the N-grams ofsearchable values from the source table, the compute service manager 108can generate a fingerprint for the search pattern by computing a hashover the N-gram of the searchable value, or a portion thereof.

As shown, consistent with these embodiments, the operation 1035 can beperformed as part of the operation 825 where the compute service manager108 prunes the scan set using the pruning index. At operation 1035, thecompute service manager 108 uses the set of N-grams generated based onthe search pattern to identify a subset of micro-portions of the sourcetable to scan based on the pruning index. The compute service manager108 may identify the subset of micro-partitions by generating a set offingerprints based on the set of N-grams (e.g., by computing a hash foreach N-gram), comparing the set of fingerprints to values included inthe pruning index (e.g., fingerprints of stored data values in thesource table), and identifying one or more values in the pruning indexthat match one or more fingerprints in the set of fingerprints generatedbased on the N-grams of the search pattern. Specifically, the computeservice manager 108 identifies one or more micro-partitions thatpotentially store data that satisfies the query based on fingerprints ofdata values in the pruning index that match fingerprints in the set offingerprints computed for the search pattern. That is, a fingerprint(e.g., hash value computed based on an N-gram of a preprocessed storeddata value in the source table) in the pruning index that matches afingerprint generated from an N-gram of the search pattern (e.g., a hashvalue computed based on the N-gram) indicates that matching data ispotentially stored in a corresponding column of the micro-partitionbecause the N-gram generated from the search pattern is stored in thecolumn of the micro-partition. The corresponding micro-partition can beidentified by the compute service manager 108 based on the matchingfingerprint in the pruning index.

Consistent with some embodiments, in identifying the subset ofmicro-partitions, the compute service manager 108 uses the pruning indexto identify any micro-partitions that contain any one of thefingerprints generated from the search pattern N-grams, and from thesemicro-partitions, the compute service manager 108 then identifies themicro-partitions that contain all the N-grams. That is, the computeservice manager 108 uses the pruning index to identify a subset ofmicro-partitions that contain data matching all fingerprints generatedbased on the N-grams of the search pattern. For example, givenfingerprints f1, f2, and f3, the compute service manager 108 uses thepruning index to determine: a first micro-partition and secondmicro-partition contain data corresponding to f1; the secondmicro-partition and a third micro-partition that contains datacorresponding to f2; and the first, second, and third micro-partitioncontain data corresponding to f3. In this example, the compute servicemanager 108 selects only the second micro-partition for scanning basedon the second micro-partition containing data that corresponds to allthree fingerprints.

As shown in FIG. 11, the method 800 may, in some embodiments, includeoperations 1105, 1110, 1115, 1120, 1125, 1130, 1135, 1140, and 1145.Consistent with these embodiments, the operations 1105, 1110, 1115, and1120 may be performed as part of operation 1010 where the computeservice manager 108 generates a set of N-grams for each preprocessedvariant of each data value in the source table. In some embodiments, theoperations 1105, 1110, 1115, and 1120 may also be performed as part ofoperation 1025 where the compute service manager 108 generates a set ofN-grams for each preprocessed variant of the search pattern.

At operation 1105, the compute service manager 108 generates a firstN-gram for a data value (e.g., a data value in the source table, apreprocessed variant of a data value in the source table, or a searchpattern included in a query) using a first value of N. Accordingly, thefirst N-gram is a first length. At operation 1110, the compute servicemanager 108 generates a second N-gram for the data value using a secondvalue of N. Hence, the second N-gram is a second length.

At operation 1115, the compute service manager 108 generates a thirdN-gram for the data value using a third value of N (an N-gram that is athird length). At operation 1120, the compute service manager 108generates a Mth N-gram for the data value using a Mth value of N (anN-gram that is an Mth length). Each N-gram in the set of N-gramsgenerated for the data value starts from the same offset, and thus, allN-grams in the set include the first N-gram as a prefix.

In an example of the foregoing operations, the first value of N is 5,the second value of N is 6, the third value of N is 7, the Mth value ofN is 8, and the data value is “testvalue.” In this example: the firstN-gram is “testy”; the second N-gram is “testva”; the third N-gram is“testval”; and the Mth N-gram is “testvalu”. Although consecutive valuesof N are described in this and other examples, it shall be appreciatedthat the prefix N-gram indexing techniques described herein are notlimited to consecutive values of N and in some embodiments,non-consecutive values of N can be used (e.g., N=5, 6, 7, 14).

Consistent with these embodiments, the operations 1125, 1130, 1135, 1140may be performed as part of operation 1015 where the compute servicemanager 108 generates a set of fingerprints based on each set ofN-grams. In some embodiments, the operations 1125, 1130, 1135, and 1140may be performed as part of operation 1030 where the compute servicemanager 108 generates a set of fingerprints for the search pattern.

At operation 1120, the compute service manager 108 generates a firstfingerprint based on the first N-gram. The compute service manager 108may generate the first fingerprint by computing a first hash over thefirst N-gram. In the “testvalue” example introduced above, the computeservice manager 108 generates the first fingerprint by computing a hashover “testy” (e.g., hash5=compute_hash (“testy”, 0)).

At operation 1125, the compute service manager 108 generates a secondfingerprint based on the second N-gram and the first fingerprint. Insome embodiments, the compute service manager 108 can generate thesecond fingerprint by computing a second hash over the second N-gram.Consistent with these embodiments, in the “testvalue” example fromabove, the compute service manager 108 can generate the secondfingerprint by computing a hash over “testva” (e.g., hash6=compute_hash(“testva”)). In some embodiments, the compute service manager 108 maygenerate the second fingerprint using the first hash as a seed for thehashing function used to compute a second hash over a portion of thesecond N-gram that excludes the first N-gram. Consistent with theseembodiments, in the “testvalue” example from above, the compute servicemanager 108 generates the second fingerprint by computing a hash over“a” using the hash of “testy” as the seed (e.g., hash6=compute_hash(“a”, hash5)).

At operation 1130, the compute service manager 108 generates a thirdfingerprint based on the third N-gram and the second fingerprint. Insome embodiments, the compute service manager 108 can generate the thirdfingerprint by computing a third hash over the third N-gram. Consistentwith these embodiments, in the “testvalue” example from above, thecompute service manager 108 can generate the third fingerprint bycomputing a hash over “testval” (e.g., hash7=compute_hash (“testval”)).In some embodiments, the compute service manager 108 may generate thethird fingerprint using the second hash as a seed for a hashing functionused to compute a third hash over a portion of the third N-gram thatexcludes the second N-gram. Consistent with these embodiments, in the“testvalue” example from above, the compute service manager 108generates the third fingerprint by computing a hash over “1” using thepreviously computed hash of “a” as the seed (e.g., hash7=compute_hash(“1”, hash6)).

At operation 1135, the compute service manager 108 generates a Mthfingerprint based on the Mth N-gram and a (M-1) fingerprint. In someembodiments, the compute service manager 108 can generate the Mthfingerprint by computing a Mth hash over the Mth N-gram. Consistent withthese embodiments, in the “testvalue” example from above, the computeservice manager 108 can generate the Mth fingerprint by computing a hashover “testvalu” (e.g., hash8=compute_hash (“testvalu”)). In someembodiments, the compute service manager 108 may generate the Mthfingerprint by using the M−1 hash as a seed for a hashing function usedto compute a Mth hash over a portion of the Mth N-gram that excludes theM−1 N-gram. Consistent with these embodiments, in the “testvalue”example, the computer service manager 108 generates the Mth fingerprintby computing a hash over “u” using the previously computed hash of “1”(e.g., hash8=compute_hash (“u”, hash7)).

Consistent with some embodiments, the operation 1145 can be performed aspart of operation 810 where the compute service manager 108 generatesthe pruning index. At operation 1145, the compute service manager 108determines, based on the first hash, a filter in the set of filters ofthe pruning index to populate using the first-Mth fingerprints. That is,fingerprints generated based on N-grams that share the first N-gram as aprefix are used to populate the same filter. In embodiments, in whichthe pruning index comprises one or more blocked bloom filters, thecompute service manager 108 can use the first hash to determine a bloomfilter block to populate using the first-Mth fingerprints (e.g., bysetting bits in the bloom filter block). By using the fingerprints topopulate the same bloom filter block in this manner, the compute servicemanager 108 can maintain low lookup costs for the pruning index.

In some embodiments, the bits for a hierarchy can be spread overmultiple bloom filter rows to address deep hierarchies (e.g.,hierarchies comprising 10 or more levels). For example, assuming ahierarchy comprising 10 levels, bits corresponding to the first 5 levelsmay be placed in a first bloom filter row while bits corresponding tothe second 5 levels can be placed in a second bloom filter row.Populating bloom filters in this manner ensures that a bloom filter rowdoes not become overpopulated because of deep hierarchical data.

As shown in FIG. 12, the method 800 may, in some embodiments, includeoperations 1205, 1210, and 1215. Consistent with these embodiments, theoperations may be performed prior to or as part of operation 810 wherethe compute service manager 108 generates a pruning index for a sourcetable. In some embodiments, the operations 1205 and 1210 may also beperformed as part of or prior to operation 825 where the compute servicemanager 108 prunes the scan set using the pruning index.

At operation 1205, the compute service manager 108 decomposes a dataitem into multiple segments. The sizes of the segments may be uniform ormay be varied. In embodiments in which the operation 1205 is performedprior to or as part of the operation 810 the data item may, for example,comprise a data value from a single column in the source table, acombination of two or more data values from different columns in thesource table, or a preprocessed variant thereof. In embodiments in whichthe operation 1205 is performed prior to or as part of the operation 825the data item may, for example, comprise a search pattern included inthe query or a preprocessed variant thereof.

In instances in which the data item comprises two or more data valuesfrom the source table, the two or more data values may have ahierarchical relationship. In a first example, the data item comprises“San Francisco, Calif.,” which corresponds to a combination of a Cityand a State, each of which may be stored in separate columns of thesource table. In a second example, the data item comprises the followinginternet protocol (IP) address: “192.168.1.40”. Generally, an IP addresscomprises a network identifier that identifies a network and a hostidentifier that identifies a device.

As shown, the operation 1205 can include operations 1206, 1207, and1208. At operation 1206, the compute service manager 108 determines aroot segment (e.g., a prefix) for the data item. In the first example,the compute service manager 108 may determine the State “California” isthe root segment of the data item. In the second example, the computeservice manager 108 may determine the network identifier “192.168.1” isthe root segment of the data item.

At operation 1207, the compute service manager 108 determines a firstchild segment of the data item and at operation 1208, the computeservice manager 108 determines an Mth child segment for the data item.Each of the child segments start from the same offset, and thus, thechild segments include the root segment as a prefix. It shall beappreciated that the number of child segments for the data itemdetermined by the compute service manager 108 depends on the type ofdata item, and in some instances, the number M of child segments may belimited to one.

In the first example discussed above, the compute service manager 108determines the city “San Francisco” is the first (and only) childsegment for the data item. In the second example discussed above, thecompute service manager 108 determines the host identifier “40” is thefirst (and only) child segment for the data item.

At operation 1210, the compute service manager 108 generates a set offingerprints for the data item based on the multiple components.Consistent with these embodiments, the operation 1210 can includeoperations 1211, 1212, and 1213. At operation 1211, the compute servicemanager 108 generates a first fingerprint based on the root segment ofthe data item. The compute service manager 108 may generate the firstfingerprint by computing a first hash over the root segment. In thefirst example discussed above, the compute service manager 108 generatesthe first fingerprint by computing a hash over “California” (e.g.,hashRoot=compute_hash (“California”, 0)). In the second examplediscussed above, the compute service manager 108 generates the firstfingerprint by computing a hash over “192.168.1” (e.g.,hashRoot=compute_hash (“192.168.1”, 0)).

At operation 1212, the compute service manager 108 generates a secondfingerprint based on the first child segment of the data item and thefirst fingerprint. The compute service manager 108 may generate thesecond fingerprint using the first hash as a seed for the hashingfunction used to compute a second hash over the first child segment ofthe data item. At operation 1213, the compute service manager 108generates a M+1 fingerprint for the data item based on the Mth childsegment and Mth fingerprint. The compute service manager 108 maygenerate the M+1 fingerprint by using the Mth hash as a seed for ahashing function used to compute a hash over the Mth child segment ofthe data item.

In the first example from above, the compute service manager 108generates the second fingerprint by computing a hash over “SanFrancisco” using the hash of “California” as the seed (e.g.,hashChild1=compute_hash (“San Francisco”, hashRoot)). In the secondexample from above, the compute service manager 108 generates the secondfingerprint by computing a hash over “40” using the hash of “192.168.1”as the seed (e.g., hashChild1=compute_hash (“40”, hashRoot)).

Consistent with some embodiments, the operation 1215 can be performed aspart of operation 810 where the compute service manager 108 generatesthe pruning index. At operation 1215, the compute service manager 108determines, based on the first hash, a filter in the set of filters ofthe pruning index to populate using the set of fingerprints generatedfor the data item. That is, fingerprints generated based on componentsof the data item that share the root segment as a prefix are used topopulate the same filter. In embodiments, in which the pruning indexcomprises one or more blocked bloom filters, the compute service manager108 can use the first hash to determine a bloom filter block to populateusing the set of fingerprints (e.g., by setting bits in the bloom filterblock). Populating a given filter, a different number of bits can beused for the fingerprint of different levels in a hierarchy. Forexample, the number of bits used to populate the filter for eachfingerprint can be gradually decreased as the level for which thefingerprint was produced is increased. By using the fingerprints topopulate the same filter in this manner, the compute service manager 108can maintain low lookup costs for the pruning index.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: accessing a source table organized intoa set of batch units; generating a set of N-grams for a data value inthe source table, the set of N-grams comprising a first N-gram of afirst length and a second N-gram of a second length, the first N-gramcorresponding to a prefix of the second N-gram; generating a set offingerprints for the data value based on the set of N-grams, the set offingerprints comprising a first fingerprint generated based on the firstN-gram and a second fingerprint generated based on the second N-gram andthe first fingerprint; and generating a pruning index that indexesdistinct N-grams in each column of the source table, the generating ofthe pruning index comprising generating a set of filters, the generatingof the set of filters comprising populating a filter using the first andsecond fingerprint.

Example 2. The method of example 1, wherein the generating of the set ofN-grams comprises: generating the first N-gram using a first value of N;and generating the second N-gram using a second value of N.

Example 3. The method of any one or more of examples 1 or 2, wherein thegenerating of the set of fingerprints comprises: generating the firstfingerprint based on the first N-gram; and generating the secondfingerprint based on the second N-gram and the first fingerprint.

Example 4. The method of any one or more of examples 1-3, wherein:generating the first fingerprint comprises computing a hash over thefirst N-gram; and generating the second fingerprint comprises computinga hash over a portion of the second N-gram that excludes the firstN-gram using the first fingerprint as a seed for a hashing function usedto compute the hash.

Example 5. The method of any one or more of examples 1-4, wherein: thegenerating of the set of N-grams for the data value further comprisesgenerating a third N-gram for the data value using a third value of N,and the generating of the set of fingerprints for the data value basedon the set of N-grams further comprises generating a third fingerprintbased on the third N-gram and the second fingerprint.

Example 6. The method of any one or more of examples 1-5, wherein thegenerating of the third fingerprint comprises computing a hash over aportion of the third N-gram that excludes the second N-gram using thesecond fingerprint as a seed for a hashing function used to compute thehash.

Example 7. The method of any one or more of examples 1-6, furthercomprising: determining, based on the hash computed over the firstN-gram, the filter from the set of filters to populate using the set offingerprints.

Example 8. The method of any one or more of examples 1-7, furthercomprising storing, in a database, the pruning index with an associationwith the source table.

Example 9. The method of any one or more of examples 1-8, furthercomprising: receiving a query directed at the source table, the queryspecifying a search pattern; pruning the set of batch units to scan fordata matching the search pattern using the pruning index, the pruning ofthe set of batch units comprising identifying a subset of batch units toscan for matching data; and processing the query by scanning the subsetof batch units.

Example 10. The method of any one or more of examples 1-9, wherein thepruning of the set of batch units includes: generating one or morefingerprints based on the search pattern; and identifying one or morevalues in the pruning index that match the one or more fingerprints.

Example 11. A system comprising: one or more processors of a machine;and at least one memory storing instructions that, when executed by theone or more processors, cause the machine to perform operationsimplementing any one of example methods 1 to 10.

Example 12. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 11.

FIG. 13 illustrates a diagrammatic representation of a machine 1300 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1300 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 13 shows a diagrammatic representation of the machine1300 in the example form of a computer system, within which instructions1316 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1300 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1316 may cause the machine 1300 to execute anyone or more operations of the method 800. As another example, theinstructions 1316 may cause the machine 1300 to implement portions ofthe functionality illustrated in any one or more of FIGS. 4-7. In thisway, the instructions 1316 transform a general, non-programmed machineinto a particular machine 1300 (e.g., the compute service manager 108,the execution platform 110, and the data storage devices 206) that isspecially configured to carry out any one of the described andillustrated functions in the manner described herein.

In alternative embodiments, the machine 1300 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1300 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1300 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1316, sequentially orotherwise, that specify actions to be taken by the machine 1300.Further, while only a single machine 1300 is illustrated, the term“machine” shall also be taken to include a collection of machines 1300that individually or jointly execute the instructions 1316 to performany one or more of the methodologies discussed herein.

The machine 1300 includes processors 1310, memory 1330, and input/output(I/O) components 1350 configured to communicate with each other such asvia a bus 1302. In an example embodiment, the processors 1310 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1313 and aprocessor 1314 that may execute the instructions 1316. The term“processor” is intended to include multi-core processors 1310 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1316 contemporaneously. AlthoughFIG. 13 shows multiple processors 1310, the machine 1300 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1330 may include a main memory 1332, a static memory 1334,and a storage unit 1336, all accessible to the processors 1310 such asvia the bus 1302. The main memory 1332, the static memory 1334, and thestorage unit 1336 store the instructions 1316 embodying any one or moreof the methodologies or functions described herein. The instructions1316 may also reside, completely or partially, within the main memory1332, within the static memory 1334, within the storage unit 1336,within at least one of the processors 1310 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1300.

The I/O components 1350 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1350 thatare included in a particular machine 1300 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1350 mayinclude many other components that are not shown in FIG. 13. The I/Ocomponents 1350 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1350 mayinclude output components 1352 and input components 1354. The outputcomponents 1352 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1354 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1350 may include communication components 1364operable to couple the machine 1300 to a network 1380 or devices 1370via a coupling 1382 and a coupling 1372, respectively. For example, thecommunication components 1364 may include a network interface componentor another suitable device to interface with the network 1380. Infurther examples, the communication components 1364 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1370 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1300 may correspond to any one ofthe compute service manager 108, the execution platform 110, and thedevices 1370 may include the data storage device 206 or any othercomputing device described herein as being in communication with thenetwork-based data warehouse system 102 or the storage platform 104.

The various memories (e.g., 1330, 1332, 1334, and/or memory of theprocessor(s) 1310 and/or the storage unit 1336) may store one or moresets of instructions 1316 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1316, when executed by theprocessor(s) 1310, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1380may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1380 or a portion of the network1380 may include a wireless or cellular network, and the coupling 1382may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1382 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1316 may be transmitted or received over the network1380 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1364) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1316 may be transmitted or received using a transmission medium via thecoupling 1372 (e.g., a peer-to-peer coupling) to the devices 1370. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1316 for execution by the machine 1300, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor implemented. For example, at leastsome of the operations of the method 800 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be in a singlelocation (e.g., within a home environment, an office environment, or aserver farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverall adaptations or variations of various embodiments. Combinations ofthe above embodiments, and other embodiments not specifically describedherein, will be apparent to those of skill in the art, upon reviewingthe above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: at least one hardware processor; and at least one memory storing instructions that cause the at least one hardware processor to perform operations comprising: accessing a source table organized into a set of batch units; decomposing a data item from the source table into multiple segments, the multiple segments comprising a root segment and a child segment; generating a set of fingerprints for the data item based on the multiple segments, the set of fingerprints comprising a first fingerprint generated based on the root segment and a second fingerprint generated based on the child segment and the first fingerprint; and generating an index for the source table based on the set of fingerprints.
 2. The system of claim 1, wherein the generating of the set of fingerprints comprises: generating the first fingerprint based on the root segment; and generating the second fingerprint based on the child segment and the first fingerprint.
 3. The system of claim 2, wherein: generating the first fingerprint comprises computing a first hash over the root segment; and generating the second fingerprint comprises computing a second hash over the child segment using the first hash as a seed for a hashing function used to compute the second hash.
 4. The system of claim 3, wherein: the child segment is a first child segment; the multiple segments further comprise a second child segment; and the generating of the set of fingerprints for the data item further comprises generating a third fingerprint based on the second child segment and the second fingerprint.
 5. The system of claim 4, wherein the generating of the third fingerprint comprises computing a third hash over the second child segment using the second hash as a seed for a hashing function used to compute the third hash.
 6. The system of claim 3, wherein: the index comprises a set of filters; and the operations further comprise determining, based on the first hash, a filter from the set of filters to populate using the set of fingerprints.
 7. The system of claim 6, wherein generating the index comprises populating the filter using the first fingerprint and second fingerprint.
 8. The system of claim 7, wherein: populating the filter using the first fingerprint comprises populating the filter with a first number of bits, populating the filter using the second fingerprint comprises populating the filter with a second number of bits.
 9. The system of claim 1, wherein the root segment and the child segment of the data item have a hierarchical relationship.
 10. The system of claim 1, wherein the operations further comprise: receiving a query directed at the source table, the query specifying a search pattern; pruning the set of batch units to scan for data matching the search pattern using the index, the pruning of the set of batch units comprising identifying a subset of batch units to scan for matching data; and processing the query by scanning the subset of batch units.
 11. A method comprising: accessing a source table organized into a set of batch units; decomposing a data item from the source table into multiple segments, the multiple segments comprising a root segment and a child segment; generating a set of fingerprints for the data item based on the multiple segments, the set of fingerprints comprising a first fingerprint generated based on the root segment and a second fingerprint generated based on the child segment and the first fingerprint; and generating an index for the source table based on the set of fingerprints
 12. The method of claim 11, wherein the generating of the set of fingerprints comprises: generating the first fingerprint based on the root segment; and generating the second fingerprint based on the child segment and the first fingerprint.
 13. The method of claim 12, wherein: generating the first fingerprint comprises computing a first hash over the root segment; and generating the second fingerprint comprises computing a second hash over the child segment using the first hash as a seed for a hashing function used to compute the second hash.
 14. The method of claim 13, wherein: the child segment is a first child segment; the multiple segments further comprise a second child segment; and the generating of the set of fingerprints for the data item further comprises generating a third fingerprint based on the second child segment and the second fingerprint.
 15. The method of claim 14, wherein the generating of the third fingerprint comprises computing a third hash over the second child segment using the second hash as a seed for a hashing function used to compute the third hash.
 16. The method of claim 13, wherein: the index comprises a set of filters; and the method further comprises determining, based on the first hash, a filter from the set of filters to populate using the set of fingerprints.
 17. The method of claim 16, wherein generating the index comprises populating the filter using the first fingerprint and second fingerprint.
 18. The method of claim 17, wherein: populating the filter using the first fingerprint comprises populating the filter with a first number of bits, populating the filter using the second fingerprint comprises populating the filter with a second number of bits.
 19. The method of claim 18, wherein the first number of bits is greater than the second number of bits.
 20. The method of claim 11, further comprising: receiving a query directed at the source table, the query specifying a search pattern; pruning the set of batch units to scan for data matching the search pattern using the index, the pruning of the set of batch units comprising identifying a subset of batch units to scan for matching data; and processing the query by scanning the subset of batch units.
 21. A computer-storage medium comprising instructions that, when executed by one or more processors of a machine, configure the machine to perform operations comprising: accessing a source table organized into a set of batch units; decomposing a data item from the source table into multiple segments, the multiple segments comprising a root segment and a child segment; generating a set of fingerprints for the data item based on the multiple segments, the set of fingerprints comprising a first fingerprint generated based on the root segment and a second fingerprint generated based on the child segment and the first fingerprint; and generating an index for the source table based on the set of fingerprints.
 22. The computer-storage medium of claim 21, wherein the generating of the set of fingerprints comprises: generating the first fingerprint based on the root segment; and generating the second fingerprint based on the child segment and the first fingerprint.
 23. The computer-storage medium of claim 22, wherein: generating the first fingerprint comprises computing a first hash over the root segment; and generating the second fingerprint comprises computing a second hash over the child segment using the first hash as a seed for a hashing function used to compute the second hash.
 24. The computer-storage medium of claim 23, wherein: the child segment is a first child segment; the multiple segments further comprise a second child segment; and the generating of the set of fingerprints for the data item further comprises generating a third fingerprint based on the second child segment and the second fingerprint.
 25. The computer-storage medium of claim 24, wherein the generating of the third fingerprint comprises computing a third hash over the second child segment using the second hash as a seed for a hashing function used to compute the third hash.
 26. The computer-storage medium of claim 23, wherein: the index comprises a set of filters; and the operations further comprise determining, based on the first hash, a filter from the set of filters to populate using the set of fingerprints.
 27. The computer-storage medium of claim 26, wherein generating the index comprises the populating the filter using the first fingerprint and second fingerprint.
 28. The computer-storage medium of claim 26, wherein: populating the filter using the first fingerprint comprises populating the filter with a first number of bits, populating the filter using the second fingerprint comprises populating the filter with a second number of bits.
 29. The computer-storage medium of claim 28, wherein the first number of bits is greater than the second number of bits.
 30. The computer-storage medium of claim 21, wherein the root segment and the child segment of the data item have a hierarchical relationship. 